Teaching

Introduces the architecture, design, and implementation of a centralized Robot System Server for managing heterogeneous fleets of autonomous robots. Students work in sprint-based teams to build fleet management, task allocation (MRTA), and map services on top of the Open-RMF framework, progressing from simulated tinyRobot fleets to TurtleBot 4 Gazebo simulations to physical TurtleBot 4 robots — all coordinated by one server and one dashboard. Co-taught with Prof. Steve Shafer (CS).

Investigates the science and engineering of mobile robots. Topics include kinematics, dynamics, numerical methods, state estimation, control, perception, localization and mapping, and motion planning. Emphasizes practical robot applications ranging from disaster response to healthcare to space exploration, with hands-on projects that integrate theory with real robotic platforms.

Investigates the modeling, design, and analysis of assistive robotic systems through a model-based design process. Covers derivation of executable specifications, hardware and software design via simulation, implementation by code generation, and continuous testing and verification. Topics include continuous and discrete dynamics, heterogeneous models, hybrid systems, stochastic models, and embedded control for assistive robots in smart environments. Course projects emphasize hands-on design with platforms including the UR12e manipulator, EduExo exoskeleton, and PincherX-100.

Examines the sensors and mathematical techniques for robotic sensing and navigation, focusing on cameras, sonars, and laser scanners. Covers dead reckoning, visual inertial odometry, GPS, inertial measurement units, Kalman filters, and particle filters as applied to the SLAM problem. A large component of the class involves programming in the ROS environment with real field robotics sensor data sets. Labs incorporate real field sensors and platforms, culminating with both an individual design project and a team-based final project.

Introduces the analysis and design of classical control systems. Examines control system objectives, modeling and mathematical description, transfer function and state-variable representations, feedback control system characteristics, system responses, and stability. Also addresses compensator design based on root-locus and frequency response methods, and modern control system design using state-variable feedback.